# NOT RUN {
data(efc)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
ggpredict(fit, terms = "c12hour")
ggpredict(fit, terms = "c12hour", full.data = TRUE)
ggpredict(fit, terms = c("c12hour", "c172code"))
ggpredict(fit, terms = c("c12hour", "c172code", "c161sex"))
# specified as formula
ggpredict(fit, terms = ~ c12hour + c172code + c161sex)
# only range of 40 to 60 for variable 'c12hour'
ggpredict(fit, terms = "c12hour [40:60]")
# using "summary()" shows that covariate "neg_c_7" is held
# constant at a value of 11.84 (its mean value). To use a
# different value, use "condition"
ggpredict(fit, terms = "c12hour [40:60]", condition = c(neg_c_7 = 20))
# to plot ggeffects-objects, you can use the 'plot()'-function.
# the following examples show how to build your ggplot by hand.
# plot predicted values, remaining covariates held constant
library(ggplot2)
mydf <- ggpredict(fit, terms = "c12hour")
ggplot(mydf, aes(x, predicted)) +
geom_line() +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .1)
# with "full.data = TRUE", remaining covariates vary between
# observations, so fitted values can be plotted
mydf <- ggpredict(fit, terms = "c12hour", full.data = TRUE)
ggplot(mydf, aes(x, predicted)) + geom_point()
# you can add a smoothing-geom to show the linear trend of fitted values
ggplot(mydf, aes(x, predicted)) +
geom_smooth(method = "lm", se = FALSE) +
geom_point()
# three variables, so we can use facets and groups
mydf <- ggpredict(
fit,
terms = c("c12hour", "c161sex", "c172code"),
full.data = TRUE
)
ggplot(mydf, aes(x = x, y = predicted, colour = group)) +
stat_smooth(method = "lm", se = FALSE) +
facet_wrap(~facet, ncol = 2)
# average marginal effects
mydf <- ggaverage(fit, terms = c("c12hour", "c172code"))
ggplot(mydf, aes(x = x, y = predicted, colour = group)) +
stat_smooth(method = "lm", se = FALSE)
# select specific levels for grouping terms
mydf <- ggpredict(fit, terms = c("c12hour", "c172code [1,3]", "c161sex"))
ggplot(mydf, aes(x = x, y = predicted, colour = group)) +
stat_smooth(method = "lm", se = FALSE) +
facet_wrap(~facet) +
labs(
y = get_y_title(mydf),
x = get_x_title(mydf),
colour = get_legend_title(mydf)
)
# level indication also works for factors with non-numeric levels
# and in combination with numeric levels for other variables
library(sjlabelled)
data(efc)
efc$c172code <- as_label(efc$c172code)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
ggpredict(fit, terms = c("c12hour",
"c172code [low level of education, high level of education]",
"c161sex [1]"))
# use categorical value on x-axis, use axis-labels, add error bars
dat <- ggpredict(fit, terms = c("c172code", "c161sex"))
ggplot(dat, aes(x, predicted, colour = group)) +
geom_point(position = position_dodge(.1)) +
geom_errorbar(
aes(ymin = conf.low, ymax = conf.high),
position = position_dodge(.1)
) +
scale_x_continuous(breaks = 1:3, labels = get_x_labels(dat))
# 3-way-interaction with 2 continuous variables
data(efc)
# make categorical
efc$c161sex <- as_factor(efc$c161sex)
fit <- lm(neg_c_7 ~ c12hour * barthtot * c161sex, data = efc)
# select only levels 30, 50 and 70 from continuous variable Barthel-Index
dat <- ggpredict(fit, terms = c("c12hour", "barthtot [30,50,70]", "c161sex"))
ggplot(dat, aes(x = x, y = predicted, colour = group)) +
stat_smooth(method = "lm", se = FALSE, fullrange = TRUE) +
facet_wrap(~facet) +
labs(
colour = get_legend_title(dat),
x = get_x_title(dat),
y = get_y_title(dat),
title = get_title(dat)
)
# or with ggeffects' plot-method
# }
# NOT RUN {
plot(dat, ci = FALSE)
# }
# NOT RUN {
# use factor levels as x-column in returned data frame
data(efc)
efc$c161sex <- as_label(efc$c161sex)
fit <- lm(neg_c_7 ~ c12hour + c161sex, data = efc)
ggpredict(fit, terms = "c161sex", x.as.factor = TRUE)
# marginal effects for polynomial terms
data(efc)
fit <- glm(
tot_sc_e ~ c12hour + e42dep + e17age + I(e17age^2) + I(e17age^3),
data = efc,
family = poisson()
)
ggeffect(fit, terms = "e17age")
# }
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